Traffic Forecasting Research Articles

ADDGCN: A Novel Approach with Down-Sampling Dynamic Graph Convolution and Multi-Head Attention for Traffic Flow Forecasting

An essential component of autonomous transportation system management and decision-making is precise and real-time traffic flow forecast. Predicting future traffic conditionsis a difficult undertaking because of the intricate spatio-temporal relationships involved. Existing techniques often employ separate modules to model spatio-temporal features independently, thereby neglecting the temporally and spatially heterogeneous features among nodes. Simultaneously, many existing methods overlook the long-term relationships included in traffic data, subsequently impacting prediction accuracy. We introduce a novel method to traffic flow forecasting based on the combination of the feature-augmented down-sampling dynamic graph convolutional network and multi-head attention mechanism. Our method presents a feature augmentation mechanism to integrate traffic data features at different scales. The subsampled convolutional network enhances information interaction in spatio-temporal data, and the dynamic graph convolutional network utilizes the generated graph structure to better simulate the dynamic relationships between nodes, enhancing the model’s capacity for capturing spatial heterogeneity. Through the feature-enhanced subsampled dynamic graph convolutional network, the model can simultaneously capture spatio-temporal dependencies, and coupled with the process of multi-head temporal attention, it achieves long-term traffic flow forecasting. The findings demonstrate that the ADDGCN model demonstrates superior prediction capabilities on two real datasets (PEMS04 and PEMS08). Notably, for the PEMS04 dataset, compared to the best baseline, the performance of ADDGCN is improved by 2.46% in MAE and 2.90% in RMSE; for the PEMS08 dataset, compared to the best baseline, the ADDGCN performance is improved by 1.50% in RMSE, 3.46% in MAE, and 0.21% in MAPE, indicating our method’s superior performance.

A context-driven approach through stakeholder engagement to introduce a digital emergency obstetric and newborn care register into routine obstetric health care services in Bangladesh.

Emergency obstetric and newborn care (EmONC) in Bangladesh focusses on maternal health, whereby it addresses childbirth and postpartum complications to ensure women's health and well-being. It was transitioned to a digital platform to overcome challenges with the paper-based EmONC register and we conducted implementation research to assess the outcome. Here we outline the stakeholder engagement process integral to the implementation research process. We adopted a four-step stakeholder engagement model based on the identification, sensitisation, involvement, and engagement of stakeholders. The approach was informed by previous experience, desk reviews, and expert consultations to ensure comprehensive engagement with stakeholders at multiple levels. Led by the Maternal Health Programme of the Government of Bangladesh, we involved high-power and high-interest stakeholders in developing a joint action plan for digitisation of the paper-based EmONC register. Finally, we demonstrated this digital EmONC register in real-life settings to stakeholders at different levels. The successful demonstration process fostered government ownership and collaboration with multiple stakeholders, while laying the foundation for scalability and sustainability. Nevertheless, our experience highlighted that the stakeholder engagement process is context-driven, time-consuming, resource-intensive, iterative, and dynamic, and it requires involving stakeholders with varied expertise. Effective strategic planning, facilitation, and the allocation of sufficient time and resources are essential components for successful stakeholder engagement. Our experience demonstrates the potential of adopting the 'identification, sensitisation, involvement, and engagement' stakeholder engagement model. Success in implementing this model in diverse settings depends on leveraging knowledge gained during implementation, maintaining robust communication with stakeholders, and harnessing the patience and determination of the facilitating organisation.

Machine Learning Models for Real-Time Traffic Prediction: A Case Study in Urban Traffic Management

This study introduces and evaluates the Long-term Traffic Prediction Network (LTPN), a specialized machine learning framework designed for real-time traffic prediction in urban environments. Utilizing a unique combination of convolutional and recurrent neural network layers, the LTPN model consistently outperforms established predictive models across various metrics. It demonstrates significantly lower error rates in both short and long-term traffic forecasts, highlighting its superior accuracy and reliability. The effectiveness of the LTPN model is underscored by its robust performance under diverse traffic conditions, making it a promising tool for enhancing the efficiency and responsiveness of intelligent transportation systems (ITS). This paper details the model's architecture, training processes, and a comprehensive comparison of its predictive capabilities against traditional models, providing clear evidence of its advantages in real-world applications.

Traffic Flow Prediction by an Ensemble Framework with Data Denoising and Functional Principal Components Analysis

Accurate traffic flow data are important for congestion identification and real-time traffic control. Raw traffic flow data may be disturbed by different noises during the acquisition process, which leads to the degradation of model prediction performance. To address this problems, this paper proposes a data denoising method based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with wavelet threshold denoising (WTD) to suppress potential outliers in the data, and then introduces functional principal component analysis to accomplish the traffic forecasting task. We use the proposed framework for real traffic flow prediction and validate the prediction performance of the proposed framework using root mean square error and mean absolute error. In the prediction performance comparison, five denoising methods are considered: empirical mode decomposition (EMD), ensemble empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, ICEEMDAN, and WTD. The prediction results show that the model combined with the denoising method outperforms the model without the denoising method. ICEEMDAN combined with WTD’s denoising method improves prediction performance more than other methods. Furthermore, the WTD method with dmey type achieves higher accuracy than other types.

Hyper-Flophet: A neural Prophet-based model for traffic flow forecasting in transportation systems

Nowadays, an accurate and reliable traffic forecast is meaningful in making the right decisions for traffic management systems in vehicular environments. Nevertheless, traffic flow prediction is a significant challenge in Vehicular Ad Hoc Networks (VANETs) that has taken much attention. Therefore, in this paper, we propose a hybrid traffic prediction model based on Prophet model and Long Short-Term Memory neural network (LSTM), called Hyper-Flophet, to predict next traffic flow. Hyper-Flophet model adopts the traditional neural prophet model but with major parameter tuning. First, we propose an efficient algorithm for predicting the traffic flow trend then, we develop an interactive LSTM (I-LSTM) model for auto-regression components. After that, we implement a new future regressor component called network mobility and finally, we enhance the event and holiday component by introducing exponential growth term. Through simulations with real VANET data, we show that the proposed hybrid approach can achieve superior forecasting performance over other models.

One-step vs horizon-step training strategies for multi-step traffic flow forecasting with direct particle swarm optimization grid search support vector regression and long short-term memory

In the increasingly complex urban transportation landscape, the necessity for accurate, multi-step forecasting has never been more apparent to help plan for long-term, strategic transportation initiatives like Intelligent Transportation Systems. This work focuses on resolving uncertainties around the proper training step size for machine learning models performing multi-step traffic flow forecasting. Two strategies are considered: one-step and horizon-step training sizes, which extend from the single-step forecasting method. This paper compares and evaluates two machine learning models: the Support Vector Regression and Long Short-Term Memory (LSTM). Data from two country road locations, including urban roads in Kuala Lumpur and the I5-North freeway in California, are employed to forecast traffic flow in 1-hour increments, projecting from 2 h up to 24 h ahead. The results reveal a significant difference in performance between the two training step sizes, with the one-step training size emerging as the more consistent and optimal strategy for both Direct and Multi-Input Multi-Output forecasting strategies. The proposed model, the Direct Particle Swarm Optimization Grid Search Support Vector Regression (Direct-PSOGS-SVR) model, exhibited comparable or slightly better performance than the LSTM model in both environments. In the I5-North freeway dataset, the Direct-PSOGS-SVR achieved similar Root Mean Squared Error values across most forecasting tasks compared to the LSTM, indicating its effectiveness in stable traffic flow patterns. Notably, in the more dynamic urban environment of Kuala Lumpur, the Direct-PSOGS-SVR model also demonstrated stability and resilience in forecasting accuracy, effectively handling the inherent noise and fluctuations in traffic patterns. These findings serve as a valuable guide for practitioners in selecting the most efficacious combination of training strategies for specific time series forecasting tasks utilizing machine learning models, particularly in traffic flow forecasting. Introducing the Direct-PSOGS-SVR model enriches the landscape of machine learning solutions for traffic forecasting, underscoring the paper’s contributions to the broader understanding of time series forecasting dynamics.

Web Traffic Time Series Forecasting of Temperature Analysis

This project makes use of the ARIMA (AutoRegressive integrated moving common) model to forecast web site visitors on weather-related web sites, analyzing how temperature fluctuations affect tourist numbers. ancient web visitors and temperature records are amassed, preprocessed, and analyzed. The ARIMA version is enhanced by way of incorporating temperature as an external regressor, optimizing forecasting accuracy via cautious parameter tuning. This method is evaluated towards traditional models to assess its effectiveness. The findings reveal that integrating temperature records notably improves predictive overall performance, supplying precious insights for managing web content based totally on environmental elements and predicting visitors developments with more precision.

Local spatial and temporal relation discovery model based on attention mechanism for traffic forecasting

Recognizing the evolution pattern of traffic condition and making accurate prediction play a vital role in intelligent transportation systems (ITS). With the massive increase of available traffic data, deep learning-based models have attracted considerable attention for their impressive performance in traffic forecasting. However, the majority of existing approaches neglect to model of asynchronously dynamic spatio-temporal correlation and fail to consider the impact of historical traffic data on future condition. Additionally, the attribute of deep learning method presents challenges in interpreting the explicit spatiotemporal relationships. In order to enhance the accuracy of traffic prediction as well as extract comprehensive and explainable spatial–temporal relevance in traffic networks, we propose a novel attention-based local spatial and temporal relation discovery (ALSTRD) model. Our model firstly implements feature representation learning to effectively express latent input traffic information. Then, a local attention mechanism structure is established to model asynchronous dependencies of historical input data. Finally, another attention network and the Pearson Correlation Coefficient method are introduced to extract the elaborate influence of the historical traffic condition of neighboring roads on the future condition of the target road. The experiment results on several datasets demonstrate that our model achieves significant improvements in prediction accuracy compared to other baseline methods, which can be attributed to its ability to extract the fine-grained correlation among historical traffic data and capture the dynamic association between past and future data. In addition, the incorporation of attention mechanism and Pearson Correlation Coefficient promotes the model’s ability to elucidate spatiotemporal correlations among traffic data, thereby providing a more robust explanation.

Enhanced Multi-Task Traffic Forecasting in Beyond 5G Networks: Leveraging Transformer Technology and Multi-Source Data Fusion

Managing cellular networks in the Beyond 5G (B5G) era is a complex and challenging task requiring advanced deep learning approaches. Traditional models focusing on internet traffic (INT) analysis often fail to capture the rich temporal and spatial contexts essential for accurate INT predictions. Furthermore, these models do not account for the influence of external factors such as weather, news, and social trends. This study proposes a multi-source CNN-RNN (MSCR) model that leverages a rich dataset, including periodic, weather, news, and social data to address these limitations. This model enables the capture and fusion of diverse data sources for improved INT prediction accuracy. An advanced deep learning model, the transformer-enhanced CNN-RNN (TE-CNN-RNN), has been introduced. This model is specifically designed to predict INT data only. This model demonstrates the effectiveness of transformers in extracting detailed temporal-spatial features, outperforming conventional CNN-RNN models. The experimental results demonstrate that the proposed MSCR and TE-CNN-RNN models outperform existing state-of-the-art models for traffic forecasting. These findings underscore the transformative power of transformers for capturing intricate temporal-spatial features and the importance of multi-source data and deep learning techniques for optimizing cell site management in the B5G era.

Evaluatıon of Toll Traffıc Revenue based on VOC and VOT Method on Bengaluru – Mysuru Natıonal Hıghway (NH-275)

Through this project report intend to bring about an economical analysis of BengaluruMysuru Expressway (NH-275) which is a stretch of 117km, constructed under Bharatmala Pariyojana (BMP) phase -1. For this economical analysis of Traffic Characteristics various study were performed. Using the studied data various estimations like liner regression method were performed for Traffic forecast (future Traffic), by traffic volume count data Average Daily Traffic (ADT) and Annual Average Daily Traffic (AADT) were considered. Estimation of Value of Time (VOT) by empirical method and evaluation of Vehicle Operating Cost (VOC) by Portuguese Road User Cost (RUC) model were used to Comparing of Toll price with VOT (Value of Time) and VOC (Vehicle Operating Cost). Revenue and Expenses from toll plaza were calculated and arrived that the company would gain profit. For the estimation of Traffic forecasting we had to study liner regression method. All the calculation made are approximate and not exact.

Adaptive Modeling of Uncertainties for Traffic Forecasting

Adaptive Modeling of Uncertainties for Traffic Forecasting

A multi-channel spatial-temporal transformer model for traffic flow forecasting

Traffic flow forecasting is a crucial task in transportation management and planning. The main challenges for traffic flow forecasting are that (1) as the length of prediction time increases, the accuracy of prediction will decrease; (2) the predicted results greatly rely on the extraction of temporal and spatial dependencies from the road networks. To overcome the challenges mentioned above, we propose a multi-channel spatial-temporal transformer model for traffic flow forecasting, which improves the accuracy of the prediction by fusing results from different channels of traffic data. Our approach leverages graph convolutional network to extract spatial features from each channel while using a transformer-based architecture to capture temporal dependencies across channels. We introduce an adaptive adjacency matrix to overcome limitations in feature extraction from fixed topological structures. Experimental results on six real-world datasets demonstrate that introducing a multi-channel mechanism into the temporal model enhances performance and our proposed model outperforms state-of-the-art models in terms of accuracy.

A novel real-time data driven method for floating vehicle speed trend prediction

Traffic congestion in urban areas has become a major worldwide problem. As an important direction of the Intelligent Transportation System (ITS), traffic-speed prediction can help drivers better plan routes and shorten travel time according to IOT techniques, thereby effectively alleviating the problem of traffic congestion. Traffic speed changes dynamically over time, so forecasting using historical data may not be able to quickly adapt to sudden changes in traffic conditions, and predicted traffic conditions may lag behind. The use of real-time data can capture instantaneous changes in traffic conditions, which is more adaptable to different traffic scenarios. In order to further explore the advantages of using real-time data for traffic forecasting, a novel real-time Data Driven method for traffic Speed trend Prediction (2DSP) is proposed. The 2DSP method can predict the macro-level traffic speed trend (rising or falling) by using only near-real-time microscopic vehicle information, which effectively captures the dynamic changes in traffic speed. In addition, an adaptive time-slicing strategy based on traffic density is proposed. This strategy dynamically divides time slices based on traffic density, reducing the frequency of data processing and improving the user experience. The effectiveness of the 2DSP method is validated using two real traffic datasets of floating vehicles. The experimental results show that the 2DSP method has good potential for real-time traffic-speed trend prediction.

Cellular traffic forecasting based on inverted transformer for mobile perception dual-level base station sleep control

Due to the extensive implementation of the fifth generation wireless communication networks (5 G), numerous base stations are being strategically deployed in densely inhabited areas to address the escalating need for network traffic. Within wireless cellular networks, over 80% of energy consumption is primarily concentrated on the side of the base station. In comparison to 4 G base stations, the 5 G base stations exhibit notable enhancements in terms of bandwidth, peak rate, and transmission power. Nonetheless, they suffer from limited signal coverage and extensive resource consumption. To address this challenge effectively, the present study introduces a Mobile Perception Dual-Level (MPDL) base station sleep control strategy based on cellular traffic forecasting. The strategy aims to satisfy the demands for reduced power consumption and enhanced service quality by monitoring both the current network traffic of the base station and the user's mobility, consequently placing the base station into two different sleep states. Specifically, we first designed an inverted Transformer (iTransformer) model to accurately forecast cellular traffic. The model is capable of effectively capturing both the spatial and temporal attributes of cellular traffic. It also takes into account the impact of geographical features, such as Points of Interest (POIs) and the quantity of Base Stations (BSs), on the forecasting outcomes. Consequently, it attains precise predictions of the cellular traffic. Based on the forecasting outcomes of cellular traffic, this study presents a mobility perception dual-level sleep strategy. This strategy proposal differentiates sleep into deep sleep and light sleep, while also taking into account the mobility attributes of users. It incorporates considerations for Quality of Service (QoS) and energy consumption. We assess the performance and application impacts of cellular traffic forecasting and base station sleep strategy in real-world datasets. Several experiments and validations have confirmed that the MPDL, as proposed in this paper, effectively decreases power consumption by a range of 22% to 41% when compared to baselines.

An efficient spatial-temporal transformer with temporal aggregation and spatial memory for traffic forecasting

Traffic forecasting technology has widespread applications in various domains, such as urban traffic planning and intelligent transportation systems. Traffic forecasting encounters challenges in effectively capturing the intricate spatial–temporal correlations in traffic data. While the latest methods have achieved satisfactory performance, they still suffer from two limitations: (i) Most methods overlook the memory of valuable traffic patterns at each traffic node, thus making it struggle to reveal dynamic spatial–temporal correlations using their inherent periodicity and trend characteristics from a broader perspective. (ii) As the research progresses, recently proposed models become increasingly complex and massive. To address these issues, we propose a Spatial-Temporal Aggregation Memory Transformer (STAMT) for traffic forecasting. Specifically, we propose a memory bank to enhance vanilla spatial attention and cache the traffic patterns of historical input. By querying these traffic patterns, which contain rich spatial–temporal semantic information, the model can optimize prediction performance by extracting trends and regularities across various periods. To reduce the computational costs, we introduce a temporal module to capture temporal correlations while reducing temporal dimension information. In addition, we leverage the random feature map and matrix multiplication associativity property, which reduce the quadratic complexity of spatial attention to linearity with regard to the number of nodes. Ultimately, our theoretical analysis concludes that a single-layer spatial attention network is sufficient to capture spatial–temporal correlations deeply without stacking. Extensive experiments on nine real-world datasets demonstrate that STAMT outperforms state-of-the-art baselines in regular, long-range, and large-scale traffic forecasting tasks while significantly reducing the computational costs. Codes are available at https://github.com/LiuAoyu1998/STAMT.

Multi-scale feature enhanced spatio-temporal learning for traffic flow forecasting

Traffic flow forecasting is a critical task within Intelligent Transportation Systems (ITS). Its main challenge lies in effectively modeling the complex traffic related big data, including intricate intra-channel and inter-channel correlations, as well as dynamic spatio-temporal dependencies. Furthermore, current methods continue to encounter bottlenecks in extracting and learning complex and dynamic spatio-temporal features from the original traffic data for long-term prediction, resulting in challenges related to model robustness and generalization. In response to these, we introduce a novel deep learning model with multi-scale feature enhancement for traffic flow forecasting, which is based on the attention mechanism and the graph convolution learning framework. We first introduce the integration design of the spatio-temporal dependency features enhancement module with the base attention learning block through a memory embedding layer. Then we propose a traffic network topology features enhancement module with the spatial attention layer, enabling dynamic enhanced learning of spatio-temporal dependency features. This comprehensive approach enables the model to effectively learn complex and dynamic spatio-temporal dependencies, capturing key patterns in traffic flow data. Through extensive experimental evaluations using traffic flow forecasting benchmarks, we have validated the superior performance of the proposed model over the state-of-the-art.

A heterogeneous traffic spatio-temporal graph convolution model for traffic prediction

Smart cities require advanced traffic management systems. Traffic forecasting is an essential task of the advanced transportation system. Traffic spatio-temporal data are often heterogeneous. Most existing traffic prediction models predominantly use separate components to extract the temporal and spatial features of traffic data. However, this overlooks the intrinsic connections between the spatio-temporal features of traffic data. To directly mine the spatio-temporal heterogeneity, this study constructs a global heterogeneous traffic spatio-temporal graph and proposes the Heterogeneous Traffic Spatio-Temporal Graph Convolution (HTSTGC). To reduce the complexity of the model, Simple Graph Convolution (SGC) is used to extract semi-structured meta-graph information. The receptive fields that capture temporal and spatial features can be flexibly adjusted separately through clever design, which can balance the performance and efficiency of the model. Finally, the feature fusion module applies Gated Graph Neural Network (GGNN) to fuse temporal and spatial features. The results on the PEMS datasets reveal that jointly modeling different types of relationships can improve the traffic prediction performance of the model. The proposed HTSTGC has better performance than the baseline methods in most cases. The research results can support urban traffic control, traffic pollution reduction, and sustainable urban development.

Proposal of a Machine Learning Approach for Traffic Flow Prediction.

Rapid global urbanization has led to a growing urban population, posing challenges in transportation management. Persistent issues such as traffic congestion, environmental pollution, and safety risks persist despite attempts to mitigate them, hindering urban progress. This paper focuses on the critical need for accurate traffic flow forecasting, considered one of the main effective solutions for containing traffic congestion in urban scenarios. The challenge of predicting traffic flow is addressed by proposing a two-level machine learning approach. The first level uses an unsupervised clustering model to extract patterns from sensor-generated data, while the second level employs supervised machine learning models. Although the proposed approach requires the availability of data from traffic sensors to realize the training of the machine learning models, it allows traffic flow prediction in urban areas without sensors. In order to verify the prediction capability of the proposed approach, a real urban scenario is considered.

Intent-based AI system in packet-optical networks towards 6G [Invited

This paper presents an intelligent dynamic network optimization system for packet-optical transport networks as the industry moves towards 6G. Such a system leverages specific artificial intelligence techniques to dynamically manage the transport network, optimize resource allocation, and guarantee quality of services. A predictive and adaptive Markov decision process is defined by exploiting an ad hoc model of optical-packet nodes and network representation used for the environment description. Comparison of statistical and neural network-based approaches is done for traffic forecasting. QL, DQL, and PPO are compared to solve the reinforcement learning problem. Challenges and opportunities of applying this system in various scenarios are discussed, and assessment is done by simulations that showed advantages in the following aspects: minimization of bandwidth usage guaranteeing quality of services with respect to a conventional system, improvement of optical offload improvement to reduce power consumption and packet processing, and efficient load balancing.

Multi-Source Information Fusion Based DLaaS for Traffic Flow Prediction

Traffic flow prediction is the key to transportation safety and efficiency. The advance in machine learning and deep learning has promoted the development of intelligent transportation systems. For example, the emergence of Deep Learning as a Service (DLaaS) has benefitted researchers a lot in dealing with large scale dataset and complex deep learning algorithms. In traffic forecasting, despite the success of deep learning-based models, there are still shortcomings, such as inadequate use of temporal and spatial traffic information, and indirect modeling of dependencies in traffic data. To address these challenges, we learn the transportation network in the form of a graph, and use graph wavelet as a key component to extract well-positioned features from the graph based on the transportation network. Compared with graph convolution, graph wavelets are very flexible and do not need to specify adjacent regions in the topological graph structure for feature extraction. At the same time, we propose to combine the multi-information fusion traffic control and guidance collaborative neural network and the results obtained are better than the benchmark algorithms. The results by comparison with several baseline methods show that our proposed method can outperform all the baseline methods.